1. Field
This disclosure relates to the operation of power systems using solar energy, such as solar farms using photovoltaic or solar-thermal technology, as well as other weather-dependent energy sources. In particular, it concerns applications using measurements to predict meteorological conditions in order to estimate power production and control power generation or delivery.
2. Background
Utilities want and need predictable, stable power generation. End-use devices function best with a steady flow of electricity. The components of the grid system (wires, transformers, etc.) are most reliable when the flow of power is constant, or at least varies slowly and predictably.
Grid operation requires that the supply and demand of electricity be matched at all hours. During normal operation, utilities use power plants in ‘regulation’ mode to match moment-to-moment changes in load and intermittent power production and ‘load following’ to match changes in power as the power demand goes through normal daily load fluctuations. Under contingency operations (for example, when a power plant or transmission line is unexpectedly out-of-service), additional “spinning reserve” and “non-spinning reserve” resources are engaged to maintain grid reliability. Fossil fuel power plants, hydropower plants, power storage facilities, and customer load reductions all provide these services to the grid.
Different power plants have different operating ranges, time response periods and cost-to-respond profiles, and have different roles within grid operations. Some can be started very quickly, such as hydroelectric power plants. Others take longer to ramp up to full production, such as natural-gas-powered turbines. Still others take even longer to increase power production, such as coal-fired plants. If a major power production facility goes off-line or significantly reduces its electricity production, or if load demand increases more significantly than expected, the utilities must respond by starting up an alternative source quickly enough to prevent loss of power for end users.
Intermittent resources significantly impact the electrical grid because fluctuations in power from intermittent resources such as solar and wind occur during normal operation. The utility industry is beginning to deploy large-scale solar farms, producing 10 MW or more power from a single geographic location. Solar power systems produce electrical power as a function of the amount of light, referred to as insolation or irradiance, incident on the component solar panels. The irradiance affects various factors in the generation of electricity from a solar power system. If the solar power system provides a significant fraction of power to a grid operating area or section of the grid, changes in irradiance can have a significant impact on the stability of the power on the grid. The power production from solar farms is very predictable during fair weather because the ramp-up at sunrise and the ramp-down at sunset can be predicted from almanac data, and is suitably gradual that backup sources can be phased in and out at a reasonable rate. Also, on dense overcast days, the power production can drop down to 10% of the clear sky power production, although the variation in solar production over short durations is not as significant.
Non-grid-connected systems that deliver power directly to end users also need to be stable because electrical equipment can malfunction or even be damaged by a fluctuating supply of power.
Technology exists for tracking major storms, and the corresponding effect on power output from a solar farm in the storm path should be easy to predict; however, the effects of other weather conditions, such as the passage of broken clouds, are presently very difficult to predict or compensate. On partly cloudy days, a solar photovoltaic farm will alternate between full production and 10% power production with ramp times down to seconds or minutes. These ramp times are too short for most common grid backup sources to be brought online from a “cold” start. In a smaller control area, there may not be enough regulation or quick changing generation to compensate. Solar thermal systems have an inherent thermal inertia that causes them to react more slowly to irradiance changes than solar photovoltaic systems; however, intermittent shading by patchy clouds can cause unacceptable instabilities in power output for these systems too.
As indicated above, forecasting is used to increase predictability in power fluctuation. Given the forecasts of load demand and intermittent production, the grid operator may start additional power plants and operate the same or other power plants at less than full capacity such that there is sufficient system flexibility to respond to fluctuations. As a result, some grid operators are currently using centralized weather forecasting to predict power output of both wind and solar facilities. In addition, if forecasting information was readily available to operators of non-grid-connected solar and wind systems, the operators could also timely activate backup sources to prevent power interruptions, or reschedule their use of sensitive equipment.
Localized differences in wind speed due to different ground levels or obstructions will affect ambient and solar panel temperature. With changes in temperature, the output power from solar panels will change even if the irradiance does not change. Thus, local landscape features can cause different panels or arrays to produce differing power outputs at any given time.
Even if the terrain is perfectly featureless, as in some plains regions, broken or moving cloud patterns can affect the power outputs and operating factors, such as Maximum Power Point (MPP) of the PV panels below. The more area that a solar farm installation covers, the more opportunities for shifting cloud patterns or fog patches to decrease the power production in a part of a solar farm. Therefore, even with several sensors of sunlight intensity distributed across the area of the solar farm, it is difficult to accurately predict the total power that will be produced by the solar farm in the next few minutes.
A cloud passing over a part of a solar farm can quickly reduce the power generation of that part from maximum to less than 10% of maximum. A transmission grid may be limited in the amount of intermittent power generation that can be interconnected and operated using current technology while maintaining North American Electric Reliability Council (NERC) reliability requirements. Also, in accordance with NERC reliability requirements, each transmission grid operating area (balancing area) is required to identify any power exchange with other balancing areas in advance and then operate their system to strictly adhere to those schedules. Based on the power plants′ storage and load response available within a balancing area, the transmission grid has a limit to the amount of load fluctuation and intermittency the system can respond to and still meet reliability requirements. Depending on these factors, sometimes a new solar farm with its natural fluctuations can be accommodated with or without forecasting and advanced utility actions. In other cases, due to these factors and other intermittent generation effects, new solar farms' natural fluctuations cannot be accommodated by the existing grid.
FIG. 1 is a diagram of a large solar installation with varying conditions for different arrays and groups of arrays within a particular geographical area. Depicted is a varied terrain, symbolically represented by line 111, with a number of solar arrays 115a-f incorporated into a distributed power system. In addition, solar panels provided on buildings 121 can also be incorporated into the distributed power system. As depicted, local conditions may affect irradiation onto the solar panels both in gross and as individual segments. Clouds are significant because they cause substantial variability in the irradiance, including local variations on any solar panels they shade.
Depicted in FIG. 1 are substantially thick clouds 141, thin cloud layers 145, and cloud patterns 149 with convective activity. As indicated by the dotted lines, the clouds create shading patterns consistent with their total density and the solar incidence angle. A thicker cloud layer would cast a darker local shadow than a thin layer, but the overall effect of thicker clouds with smaller horizontal extent may be less significant than that of a thin layer with greater horizontal extent. On the other hand, there are circumstances in which substantial clouds, such as a towering cumulus cloud 149, may have no present effect at all on the photovoltaic network.
Since there is movement of the clouds, it is possible to predict the future positions of these clouds based on their current movement. Thus, if the clouds are moving to the right in the image, corresponding changes in solar irradiation can be expected. Similarly, there are circumstances in which the density of clouds will change over a time period represented by the movement. These changes can be fairly predictable, based on current meteorological conditions and historical meteorological data. Examples of meteorological conditions include effects of wind and wind direction in areas near mountain ridges, stability of the air (a function of the environmental lapse rate), and time of day. Many of these meteorological conditions interact; for example, an upslope wind in warm unstable air in the afternoon is likely to result in rapid cloud formation. As another example, the towering cumulus cloud 149 has no present effect on the photovoltaic network in FIG. 1 because it is not shading any part of the network; however, if the wind blows from the left side of the figure, it will have some effect as it passes, consistent with its size. If atmospheric conditions are sufficiently unstable, e.g., a hot and humid summer afternoon, it can also be predicted that as the cloud passes the affected area, it may develop into thunderstorm activity, with substantially wider coverage.
Utility regulation significantly affects the production of energy from intermittent resources such as solar power. Based on utility and regulatory methods, there may also be some value assigned for how reliably it can provide power when power is needed (capacity value). A dispatchable (controllable) power plant can be under contract to provide both energy and power at the same time. For example, a 100 MW turbine might produce 80 MW of power delivered to the grid to supply energy and then have an additional 20 MW which can be used to provide ancillary services. For example, with frequency regulation, the grid operator sends signals on an ongoing basis, identifying what power level between 80 MW and 100 MW to operate the turbine until the next signal is received. In spinning reserve or non-spinning reserve, the 20 MW is held in reserve and can be provided to the grid on very short notice.
As part of the agreement to connect a power plant to the transmission grid, a power plant may be required to limit changes in the total electricity production at the point of interconnecting to the grid (production ramp). Therefore, a need exists for means of stabilizing the output from intermittent sources such as solar farms.
SENER Ingenieria y Sistemas S.A., of Getxo, Spain, provides a software package called SENSOL that uses historical weather data to predict overall solar farm performance. It does not, however, address the need for solar farms to anticipate and react to rapidly changing conditions in real time. A tool that would perform this function for solar farms and the utilities they serve would be a valuable contribution to the commercialization of large-scale solar power plants at a cost that could effectively compete with less-sustainable power sources.
Planes and boats have on-board weather radar to detect local variations in weather patterns. Also, local weather stations have been deployed for specialized applications such as detecting wind shear and microbursts near airports. These forms of local weather detection have not been applied to use in the prediction of distributed power systems, and have not been used to predict the effects of cloud cover on solar farms or wind farms. More generally, previous systems have not employed local weather detection and local cloud prediction to predict power production dynamics on a moment-by-moment basis.
Solar farms and wind farms have set up local monitoring stations for resource assessment and performance supervision, but this monitoring has not included local weather detection and local cloud prediction to predict power production in real time. This type of real-time data gathering and power prediction would be valuable, especially if linked to a system that enabled corrective action to stabilize farm power output or grid power in the event of an unacceptable degree of expected fluctuation.